A Fuzzy Trust Management Mechanism with Dynamic Behavior Monitoring for Wireless Sensor Networks

Traditional wireless sensor networks(WSNs)are typically deployed in remote and hostile environments for information collection.The wireless communication methods adopted by sensor nodes may make the network highly vulnerable to various attacks.Traditional encryption and authentication mechanisms cannot prevent attacks launched by internal malicious nodes.The trust-based security mechanism is usually adopted to solve this problem in WSNs.However,the behavioral evidence used for trust estimation presents some uncertainties due to the open wireless medium and the inexpensive sensor nodes.Moreover,how to efficiently collect behavioral evidences are rarely discussed.To address these issues,in this paper,we present a trust management mechanism based on fuzzy logic and a cloud model.First,a type-II fuzzy logic system is used to preprocess the behavioral evidences and alleviate uncertainty.Then,the cloud model is introduced to estimate the trust values for sensor nodes.Finally,a dynamic behavior monitoring protocol is proposed to provide a balance between energy conservation and safety assurance.Simulation results demonstrate that our trust management mechanism can effectively protect the network from internal malicious attacks while enhancing the energy efficiency of behavior monitoring.

Is tumor necrosis factor-α monoclonal therapy with proactive therapeutic drug monitoring optimized for inflammatory bowel disease? Network meta-analysis

BACKGROUND The efficacy and safety of anti-tumor necrosis factor-α(TNF-α)monoclonal antibody therapy[adalimumab(ADA)and infliximab(IFX)]with therapeutic drug monitoring(TDM),which has been proposed for inflammatory bowel disease(IBD)patients,are still controversial.AIM To determine the efficacy and safety of anti-TNF-αmonoclonal antibody therapy with proactive TDM in patients with IBD and to determine which subtype of IBD patients is most suitable for proactive TDM interventions.METHODS As of July 2023,we searched for randomized controlled trials(RCTs)and observa-tional studies in PubMed,Embase,and the Cochrane Library to compare anti-TNF-αmonoclonal antibody therapy with proactive TDM with therapy with reactive TDM or empiric therapy.Pairwise and network meta-analyses were used to determine the IBD patient subtype that achieved clinical remission and to determine the need for surgery.RESULTS This systematic review and meta-analysis yielded 13 studies after exclusion,and the baseline indicators were balanced.We found a significant increase in the number of patients who achieved clinical remission in the ADA[odds ratio(OR)=1.416,95%confidence interval(CI):1.196-1.676]and RCT(OR=1.393,95%CI:1.182-1.641)subgroups and a significant decrease in the number of patients who needed surgery in the proactive vs reactive(OR=0.237,95%CI:0.101-0.558)and IFX+ADA(OR=0.137,95%CI:0.032-0.588)subgroups,and the overall risk of adverse events was reduced(OR=0.579,95%CI:0.391-0.858)according to the pairwise meta-analysis.Moreover,the network meta-analysis results suggested that patients with IBD treated with ADA(OR=1.39,95%CI:1.19-1.63)were more likely to undergo TDM,especially in comparison with patients with reactive TDM(OR=1.38,95%CI:1.07-1.77).CONCLUSION Proactive TDM is more suitable for IBD patients treated with ADA and has obvious advantages over reactive TDM.We recommend proactive TDM in IBD patients who are treated with ADA.

A study on temperature monitoring method for inverter IGBT based on memory recurrent neural network

The power module of the Insulated Gate Bipolar Transistor(IGBT)is the core component of the traction transmission system of high-speed trains.The module's junction temperature is a critical factor in determining device reliability.Existing temperature monitoring methods based on the electro-thermal coupling model have limitations,such as ignoring device interactions and high computational complexity.To address these issues,an analysis of the parameters influencing IGBT failure is conducted,and a temperature monitoring method based on the Macro-Micro Attention Long Short-Term Memory(MMALSTM)recursive neural network is proposed,which takes the forward voltage drop and collector current as features.Compared with the traditional electricalthermal coupling model method,it requires fewer monitoring parameters and eliminates the complex loss calculation and equivalent thermal resistance network establishment process.The simulation model of a highspeed train traction system has been established to explore the accuracy and efficiency of MMALSTM-based prediction methods for IGBT power module junction temperature.The simulation outcomes,which deviate only 3.2% from the theoretical calculation results of the electric-thermal coupling model,confirm the reliability of this approach for predicting the temperature of IGBT power modules.

Discontinuity development patterns and the challenges for 3D discrete fracture network modeling on complicated exposed rock surfaces

Natural slopes usually display complicated exposed rock surfaces that are characterized by complex and substantial terrain undulation and ubiquitous undesirable phenomena such as vegetation cover and rockfalls.This study presents a systematic outcrop research of fracture pattern variations in a complicated rock slope,and the qualitative and quantitative study of the complex phenomena impact on threedimensional(3D)discrete fracture network(DFN)modeling.As the studies of the outcrop fracture pattern have been so far focused on local variations,thus,we put forward a statistical analysis of global variations.The entire outcrop is partitioned into several subzones,and the subzone-scale variability of fracture geometric properties is analyzed(including the orientation,the density,and the trace length).The results reveal significant variations in fracture characteristics(such as the concentrative degree,the average orientation,the density,and the trace length)among different subzones.Moreover,the density of fracture sets,which is approximately parallel to the slope surface,exhibits a notably higher value compared to other fracture sets across all subzones.To improve the accuracy of the DFN modeling,the effects of three common phenomena resulting from vegetation and rockfalls are qualitatively analyzed and the corresponding quantitative data processing solutions are proposed.Subsequently,the 3D fracture geometric parameters are determined for different areas of the high-steep rock slope in terms of the subzone dimensions.The results show significant variations in the same set of 3D fracture parameters across different regions with density differing by up to tenfold and mean trace length exhibiting differences of 3e4 times.The study results present precise geological structural information,improve modeling accuracy,and provide practical solutions for addressing complex outcrop issues.

A three-dimensional environmental monitoring system for the production of marine gas hydrates

1.Objectives As about 99% of the natural gas hydrate (NGH)resources are stored on the seafloor,it is inevitable that in its process of exploration and production,there will be geohazards such as methane leakage,seabed subsidence,ecosystem damage and even landslide.Therefore,a set of integrated systems for environmental monitoring in the process of NGH production has been proposed in the consideration of environmental disasters that may arise in the process of hydrate production.

Three-Dimensional Cooperative Localization via Space-Air-Ground Integrated Networks

The space-air-ground integrated network(SAGIN)combines the superiority of the satellite,aerial,and ground communications,which is envisioned to provide high-precision positioning ability as well as seamless connectivity in the 5G and Beyond 5G(B5G)systems.In this paper,we propose a three-dimensional SAGIN localization scheme for ground agents utilizing multi-source information from satellites,base stations and unmanned aerial vehicles(UAVs).Based on the designed scheme,we derive the positioning performance bound and establish a distributed maximum likelihood algorithm to jointly estimate the positions and clock offsets of ground agents.Simulation results demonstrate the validity of the SAGIN localization scheme and reveal the effects of the number of satellites,the number of base stations,the number of UAVs and clock noise on positioning performance.

Polynomials of Degree-Based Indices for Three-Dimensional Mesh Network

In order to study the behavior and interconnection of network devices,graphs structures are used to formulate the properties in terms of mathematical models.Mesh network(meshnet)is a LAN topology in which devices are connected either directly or through some intermediate devices.These terminating and intermediate devices are considered as vertices of graph whereas wired or wireless connections among these devices are shown as edges of graph.Topological indices are used to reflect structural property of graphs in form of one real number.This structural invariant has revolutionized the field of chemistry to identify molecular descriptors of chemical compounds.These indices are extensively used for establishing relationships between the structure of nanotubes and their physico-chemical properties.In this paper a representation of sodium chloride(NaCl)is studied,because structure of NaCl is same as the Cartesian product of three paths of length exactly like a mesh network.In this way the general formula obtained in this paper can be used in chemistry as well as for any degree-based topological polynomials of three-dimensional mesh networks.

A Novel Hydrogen-bonded Three-dimensional Network Complex Containing Nickel

A novel complex, (H 3O) 2[Ni(2,6-pydc) 2]·2H 2O was synthesized in an aqueous solution and characterized by means of single-crystal X-ray diffraction, elemental analyses and IR spectra. The X-ray structural analysis revealed that the novel compound forms three-dimensional(3D) networks by both π-π stacking and hydrogen-bonding interactions. The crystal data for the complex are a=13.853(3) nm, b=9.6892(19) nm, c=13.732(3) nm, α=90.00°, β=115.52(3)°, γ=90.00°, Z=3, R 1=0.0786, wR 2=0.1522.

Well-Drilling and Groundwater Monitoring Network Construction:Taking Changde City as an Example

Entrusted by the Environmental Protection Bureau of Changde City,we conducted drilling,sampling survey and constructed a monitoring network for groundwater in several counties and districts of Changde City.This article introduces the drilling technology,detection method and detection network layout plan adopted in the project,and expounds the problems that occurred while executing the project,in order to provide reference for similar groundwater capacity supervision and construction projects.

A New Three-dimensional Network Constructed by Heptamolybdate, Sodium Ions and Hexamethylene Tetramine Cations via Hydrogen Bonds

The crystal structure of the title compound [Na2(OH2)5]2+[C6H12N4H2]2-2+ [Mo7O24]6 ?4H2O, prepared from an aqueous solution of Na2MoO4 ?2H2O in the presence of MoCl3 and hexamethylene tetramine, has been determined by single-crystal X-ray diffraction. The crystal is of orthorhombic, space group Pnma with a = 14.6113(2), b = 18.6833(1), c = 15.3712(2), V = 4196.14(8)3, Z = 4, Mr = 1548.13, F(000) = 3016, = 2.157 mm-1 and Dc = 2.451 g/cm3. The final R factor is 0.0526 for 3818 unique observed reflections (I > 2(I)). The structural analysis reveals that heptamolybdate anions in the title compound consist of seven edge-sharing MoO6 octahedra, and are linked into a three-dimensional framework by sodium ions and hydrogen bonds.

A New Eight-connected Three-dimensional Network Based on a Tetranuclear Zinc Cluster Building Block

One interesting coordination polymer, [Zn2（1,2,4-BTC）（OH）（H2O）2]2·2H2O 1, has been synthesized from 1,2,4-BTC （1,2,4-BTC = 1,2,4-bentricarboxylate） under hydrothermal conditions and characterized by elemental analyses, IR, TG and single-crystal X-ray diffraction. Complex I crystallizes in triclinic, space group P^-1, with a = 6.5200（13）, b = 9,0600（18）, c = 10.968（2） A^°, α = 111.55（3）, β = 92.07（3）,γ= 95.03（3）°, C9H10O10Zn2, Mr = 408.91, V= 598.7（2） A^°^3, Dc = 2.268 g/cm^3, F（000） = 408 and Z = 2. X-ray diffraction analysis reveals that complex 1 is a three-dimensional network built from tetranuclear Zn（Ⅱ） building unit. In this complex, the Zn4 unit is an eight-connected knot, while 1,2,4-BTC a four-connected knot. This results in a CaF2 topology. To the best of our knowledge, such Zn4 unit is the first 8-connected building block built from asymmetry ligand.

A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

A Three-dimensional IP-based Telecom Metropolitan Area Network Model

The Metropolitan Area Network (MAN) has faced serious problems after years of rapid development. The model of three-dimensional IP-based MAN, proposed by ZTE, is a next-generation MAN solution, which not only solves the existing problems but also brings new ideas for the development of next-generation MAN.

Prediction of Salinity Variations in a Tidal Estuary Using Artificial Neural Network and Three-Dimensional Hydrodynamic Models

The simulation of salinity at different locations of a tidal river using physically-based hydrodynamic models is quite cumbersome because it requires many types of data, such as hydrological and hydraulic time series at boundaries, river geometry, and adjusted coefficients. Therefore, an artificial neural network (ANN) technique using a back-propagation neural network (BPNN) and a radial basis function neural network (RBFNN) is adopted as an effective alternative in salinity simulation studies. The present study focuses on comparing the performance of BPNN, RBFNN, and three-dimensional hydrodynamic models as applied to a tidal estuarine system. The observed salinity data sets collected from 18 to 22 May, 16 to 22 October, and 26 to 30 October 2002 (totaling 4320 data points) were used for BPNN and RBFNN model training and for hydrodynamic model calibration. The data sets collected from 30 May to 2 June and 11 to 15 November 2002 (totaling 2592 data points) were adopted for BPNN and RBFNN model verification and for hydrodynamic model verification. The results revealed that the ANN (BPNN and RBFNN) models were capable of predicting the nonlinear time series behavior of salinity to the multiple forcing signals of water stages at different stations and freshwater input at upstream boundaries. The salinity predicted by the ANN models was better than that predicted by the physically based hydrodynamic model. This study suggests that BPNN and RBFNN models are easy-to-use modeling tools for simulating the salinity variation in a tidal estuarine system.

Efficient Bayesian networks for slope safety evaluation with large quantity monitoring information

New sensing and wireless technologies generate massive data. This paper proposes an efficient Bayesian network to evaluate the slope safety using large-quantity field monitoring information with underlying physical mechanisms. A Bayesian network for a slope involving correlated material properties and dozens of observational points is constructed.

Experimental and measured research on three-dimensional deformation law of gas drainage borehole in coal seam

Using self-researched gas drainage borehole stability dynamic monitoring device, three-dimensional deformation characteristics of borehole under steady vertical load were researched experimentally and systematically. This research indicated that under the action of steady loading, the mechanical deformation path of the simulated gas drainage borehole is gradually complicated, and the decay of the borehole circumferential strain is an important characterization of the prediction and early warning of borehole instability and collapse. The horizontal position of borehole occurs compressive strain, and the vertical of which occurs tensile strain under the action of vertical stress. At the initial stage of loading, the vertical strain is more sensitive than that in the horizontal direction. After a certain period of time, the horizontal strain is gradually higher than the vertical one, and the intersection of the borehole horizontal diameter and the hole wall is the stress concentration point. With the increase of the depth of hole, the strain shows a gradual decay trend as a whole, and the vertical strain decays more observably, but there is no absolute position correlation between the amount of strain decay and the increase in borehole depth,and the area within 1.5 times the orifice size is the borehole stress concentration zone.

Current situation and trend of marine data buoy and monitoring network technology of China

Marine data buoy can provide a long-term, continuous, real-time, reliable data of ocean observation in a variety of complex marine environment. It is one of the most reliable, most effective and important means of ocean monitoring technology. In this paper, the classification, main theory and technology system of marine data buoy are summarized. The typical technological breakthrough of the development of marine data buoy in recent years is summarized. The composition and application of marine monitoring network in China was introduced, and the gap between the technology of China＇s marine data buoy and the international advanced countries is compared.Combined on the situation and demand of China＇s current situation and needs, the development trend of marine data buoy and buoy monitoring network are expected.

Upgrading a regional groundwater level monitoring network for Beijing Plain,China

Monitoring of regional groundwater levels provides important information for quantifying groundwater depletion and assessing impacts on the environment. Historically, groundwater level monitoring wells in Beijing Plain, China, were installed for assessing groundwater resources and for monitoring the cone of depression. Monitoring wells are clustered around well fields and urban areas. There is urgent need to upgrade the existing monitoring wells to a regional groundwater level monitoring network to acquire information for integrated water resources management. A new method was proposed for designing a regional groundwater level monitoring network. The method is based on groundwater regime zone mapping. Groundwater regime zone map delineates distinct areas of possible different groundwater level variations and is useful for locating groundwater monitoring wells. This method was applied to Beijing Plain to upgrade a regional groundwater level monitoring network.

Power Line Monitoring Data Transmission Using Wireless Sensor Network

The WSN used in power line monitoring is long chain structure, and the bottleneck near the Sink node is more obvious. In view of this, A Sink nodes’ cooperation mechanism is presented. The Sink nodes from different WSNs are adjacently deployed. Adopting multimode and spatial multiplexing network technology, the network is constructed into multi-mode-level to achieve different levels of data streaming. The network loads are shunted and the network resources are rationally utilized. Through the multi-sink nodes cooperation, the bottlenecks at the Sink node and its near several jump nodes are solved and process the competition of communication between nodes by channel adjustment. Finally, the paper analyzed the method and provided simulation experiment results. Simulation results show that the method can solve the funnel effect of the sink node, and get a good QoS.

Identification of Contaminant Source Characteristics and Monitoring Network Design in Groundwater Aquifers: An Overview

The groundwater system is often polluted by different sources of contamination where the sources are difficult to detect. The presence of contamination in groundwater poses significant challenges to its delineation and quantification. The remediation of a contaminated site requires an optimal decision making system to identify the pollutant source characteristics accurately and efficiently. The source characteristics are generally identified using contaminant concentration measurements from arbitrary or planned monitoring locations. To effectively characterize the sources of pollution, the monitoring locations should be selected appropriately. An efficient monitoring network will result in satisfactory characterization of contaminant sources. On the other hand, an appropriate design of monitoring network requires reliable source characteristics. A coupled iterative sequential source identification and dynamic monitoring network design, improves substantially the accuracy of source identification model. This paper reviews different source identification and monitoring network design methods in groundwater contaminant sites. Further, the models for sequential integration of these two models are presented. The effective integration of source identification and dedicated monitoring network design models, distributed sources, parameter uncertainty, and pollutant geo-chemistry are some of the issues which need to be addressed in efficient, accurate and widely applicable methodologies for identification of unknown pollutant sources in contaminated aquifers.